Fig. 6.

Multitask learning shared weights. (A) The MTL model explains 28.7% variance across all domains. (B) The top 200 weights for the MTL shared features are visualized in the brain. (C) Weights are divided into four groups: interhemispheric positive, interhemispheric negative, intrahemispheric positive, and intrahemispheric negative. (D) Weights visualized by RSN. Node sizes are proportional to the average contribution of all within-network connections. Edge thicknesses are proportional to the average weighting of all between-network connections. (E) Shared weights are projected to the 324 surface parcels.